

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						def wrapper(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__			):
												snake_case_  =      timeit.default_timer()
												snake_case_  =      func(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__			)
												snake_case_  =      timeit.default_timer() - starttime
												return delta

						snake_case_  =      func.__name__

						return wrapper


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=100    ,						SCREAMING_SNAKE_CASE__=None			):
						snake_case_  =      []
						snake_case_  =      seq_shapes or {}
						for i in range(SCREAMING_SNAKE_CASE__			):
												snake_case_  =      {}
												for col_id, (k, v) in enumerate(features.items()			):
																		if isinstance(SCREAMING_SNAKE_CASE__    ,						_ArrayXD			):
																								snake_case_  =      np.random.rand(*v.shape			).astype(v.dtype			)
																		elif isinstance(SCREAMING_SNAKE_CASE__    ,						datasets.Value			):
																								if v.dtype == "string":
																														snake_case_  =      '''The small grey turtle was surprisingly fast when challenged.'''
																								else:
																														snake_case_  =      np.random.randint(10    ,						size=1			).astype(v.dtype			).item()
																		elif isinstance(SCREAMING_SNAKE_CASE__    ,						datasets.Sequence			):
																								while isinstance(SCREAMING_SNAKE_CASE__    ,						datasets.Sequence			):
																														snake_case_  =      v.feature
																								snake_case_  =      seq_shapes[k]
																								snake_case_  =      np.random.rand(*SCREAMING_SNAKE_CASE__			).astype(v.dtype			)
																		snake_case_  =      data

												dummy_data.append((i, example)			)

						return dummy_data


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=100    ,						SCREAMING_SNAKE_CASE__=None			):
						snake_case_  =      generate_examples(SCREAMING_SNAKE_CASE__    ,						num_examples=SCREAMING_SNAKE_CASE__    ,						seq_shapes=SCREAMING_SNAKE_CASE__			)

						with ArrowWriter(features=SCREAMING_SNAKE_CASE__    ,						path=SCREAMING_SNAKE_CASE__			) as writer:
												for key, record in dummy_data:
																		snake_case_  =      features.encode_example(SCREAMING_SNAKE_CASE__			)
																		writer.write(SCREAMING_SNAKE_CASE__			)

												snake_case_,       snake_case_  =      writer.finalize()

						if not num_final_examples == num_examples:
												raise ValueError(
												    F'''Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.'''			)

						snake_case_  =      datasets.Dataset.from_file(filename=SCREAMING_SNAKE_CASE__    ,						info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE__			)			)

						return dataset





'''simple docstring'''


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_					=   {
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_					=   [
      "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TrajectoryTransformerModel",
      "TrajectoryTransformerPreTrainedModel",
      "load_tf_weights_in_trajectory_transformer",
  ]


if TYPE_CHECKING:
  from .configuration_trajectory_transformer import (
      TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TrajectoryTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_trajectory_transformer import (
        TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TrajectoryTransformerModel,
        TrajectoryTransformerPreTrainedModel,
        load_tf_weights_in_trajectory_transformer,
    )


else:
  import sys

  UpperCAmelCase_					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



